Modelling of emerging threates and epidemics
Sebastian Funk
https://epiforecasts.io
Models are a tool to combine data (what we know ) with assumptions and theory (what we think ) to learn about what we don’t know .
As an example, imagine you’re in early 2020 in the UK and you see the early reports coming in from Wuhan, you’ve got your set of contact tracers and you’re wondering: are we going to be able to contain this at the source with contact tracing?
We illustrate the potential impact that flawed model inferences can have on public health policy with the model described […] by Joel Hellewell and colleagues, which is part of the scientific evidence informing the UK Government’s response to COVID-19.
Gudrasani & Ziauddeen, Lancet Glob Health , 2020
“All models are wrong, but some are useful”
George Box
“All models are wrong , but some are useful ”
wrong : how wrong?
some : which ones?
Evaluation of forecasts
Assess quality of models by how closely prediction matches reality
Forecast hubs support systematic collection of forecasts
Reich et al., Am J Public Health, 2022
Hub aims: 1. Provide decision-makers and general public with reliable information about where the pandemic is headed in the next month. 2. Gain insight into which modelling approaches do well. (Secondarily, hold models “accountable”.) 3. Assess the reliability of forecasts for different measures of disease severity. 4. Create a community of infectious disease modelers underpinned by an open-science ethos.
Not all modelling is forecasting, can we evaluate other models?
Any model of the future is a prediction and can be evaluated as such.
Howerton et al., Nat Comm, 2023
Utility is not being assessed by modellers
Collaboration with Robert Koch Institute / WHO.
Outlook: how can we improve modelling in future epidemics
Predictable tasks during epidemics
Figure courtesy of Adam Kucharski
Summary / discussion points
Models aren’t crystal balls — but used carefully, they can give us a glimpse of the near future and guide decision-making.
We must evaluate both their accuracy and their real-world utility.
We should invest in modelling infrastructure that’s flexible, reusable, and ready.